{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing and plotting ICEsat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1. Masked with water masks that are using in the ICESat 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ICESat-2 is using the water mask products from HYDROLakes and others, while ICESat-1 has no water mask.\n",
    "This part is applying the same water masks from ICESat-2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the ICESat1-dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "import h5py\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.io.img_tiles as cimgt\n",
    "%matplotlib widget\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "### for geospatial analysis\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def glah14_to_df(filename,bounds):\n",
    "    ## sp_ex = [103.643, 104.667, 12.375, 13.287]\n",
    "    ## Bounds are [Longitude_West, Longitude_East, Latitude_South, Latitude_North]\n",
    "    f = h5py.File(filename, 'r')\n",
    "    lat = f['Data_40HZ']['Geolocation']['d_lat'][:]\n",
    "    lon = f['Data_40HZ']['Geolocation']['d_lon'][:]\n",
    "    elev = f['Data_40HZ']['Elevation_Surfaces']['d_elev'][:]\n",
    "    sec = f['Data_40HZ']['Elevation_Corrections']['d_satElevCorr'][:]\n",
    "    scf = f['Data_40HZ']['Quality']['sat_corr_flg'][:]\n",
    "    satndx = f['Data_40HZ']['Quality']['i_satNdx'][:]\n",
    "    dem = f['Data_40HZ']['Geophysical']['d_DEM_elv'][:]\n",
    "    \n",
    "    glah14_df = pd.DataFrame({'Latitude':lat,'Longitude':lon,'Elevation':elev,\n",
    "                            's_El_Corr':sec, 's_Corr_f':scf,'in_sat':satndx,\n",
    "                            'DEM':dem})\n",
    "    #### Subsetting\n",
    "    glah14_df_subset = glah14_df.loc[(glah14_df['Longitude']>=bounds[0]) \n",
    "                          & (glah14_df['Longitude']<=bounds[1])\n",
    "                          & (glah14_df['Latitude']>=bounds[2])\n",
    "                          & (glah14_df['Latitude']<=bounds[3])]\n",
    "    return glah14_df_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### load files\n",
    "## set the directory\n",
    "data_home = Path('/home/jovyan/ICESat_water_level/extraction/icesat/')\n",
    "## list them up and check them\n",
    "files= list(data_home.glob('*.H5'))\n",
    "### Spatial Bounds: \n",
    "tsl_sp_ex = [103.643, 104.667, 12.375, 13.287]\n",
    "### test track of ICESat\n",
    "test_df = glah14_to_df(files[1],tsl_sp_ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the water mask file and map it to check\n",
    "\n",
    "I clipped the sample TSL areas from HYDROLakes for this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsl_wm = gpd.read_file('/home/jovyan/ICESat_water_level/extraction/shp/tsl_sample_dis.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7c60fcd819243a2a16fb051025ad732",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### just mapping\n",
    "fig, ax = plt.subplots(figsize = (3,3))\n",
    "tsl_wm.plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a143f65f0f34184aeceb304e7f354a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tracks on TSL')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Mapping with background map\n",
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent([103, 105, 12, 14], crs=ccrs.PlateCarree())\n",
    "tsl_wm.plot(ax=ax)\n",
    "\n",
    "request = cimgt.Stamen('terrain-background')\n",
    "ax.add_image(request, 9)\n",
    "plt.title(\"Tracks on TSL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crop the ICESat 1 and map it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.GeoDataFrame(test_df)\n",
    "gdf.set_geometry(\n",
    "    gpd.points_from_xy(gdf['Longitude'], gdf['Latitude']),\n",
    "    inplace=True, crs='EPSG:4326')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just Mapping it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ed3b0e6609b4b48864d2325b45f1283",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tracks on TSL')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Mapping with background map\n",
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent([103, 105, 12, 14], crs=ccrs.PlateCarree())\n",
    "gdf.plot(ax=ax)\n",
    "\n",
    "request = cimgt.Stamen('terrain-background')\n",
    "ax.add_image(request, 9)\n",
    "plt.title(\"Tracks on TSL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clip the ICESat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_wm = gpd.clip(gdf, tsl_wm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7512e99aa1d0420a8cfd747c17c5cdc7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Water masked track of ICESat1')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Mapping with background map\n",
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent([103, 105, 12, 14], crs=ccrs.PlateCarree())\n",
    "is_wm.plot(ax=ax)\n",
    "\n",
    "request = cimgt.Stamen('terrain-background')\n",
    "ax.add_image(request, 8)\n",
    "plt.title(\"Water masked track of ICESat1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "561985c0a6614a99ba0255437c812401",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(5,4))\n",
    "ax = fig.add_subplot(111)\n",
    "### before cropping\n",
    "ax.plot(gdf['Latitude'],gdf['Elevation'],'.',markersize=1, label='all heights')\n",
    "### after cropping\n",
    "ax.plot(is_wm['Latitude'],is_wm['Elevation'],'.', color = 'orange',markersize=3, label='water heights')\n",
    "h_leg=ax.legend()\n",
    "plt.title('Heights from ICESat')\n",
    "ax.set_xlabel('Latitude')\n",
    "ax.set_ylabel('Elevation, m')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2. Self-mask: Processing Steps from Wang et al., (2013)\n",
    "\n",
    "1. Indentifying Lake Water Footprint (LWF)\n",
    "2. The slope of along track height was calculated for each track\n",
    "3. The trend of lake water-level change was fitted as a line with the least squares methods (with observations from more than 6 campaigns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Identifying LWF\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:9286b41b-ceee-45b9-a5fd-a202878b037e.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Slope calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing outliers\n",
    "\n",
    "outliers = absolute value of residual exceeding 2.5 times of standard deviations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting\n",
    "\n",
    "Fitting an equation of water surface elevation vs latitude\n",
    "\n",
    "H = A*x + B\n",
    "\n",
    "where\n",
    "H: the elevation\n",
    "A: the slope\n",
    "B: intercept\n",
    "x: latitude"
   ]
  }
 ],
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